Motion sickness is a common phenomenon, with close to two-thirds of the population experiencing it in their lifetime. With the advent of automated vehicles in the market, it is anticipated to become an even greater problem as the passengers face a lack of predictability of motion and loss of control over the vehicle. This could nullify the host of possible benefits that automated vehicles propose to offer, and therefore affect their acceptance among potential users.

It is well known that the nauseogenicity of imposed motion is dependent on the frequency content of endured accelerations, with low-frequency accelerations being the primary contributor. This thesis presents a motion planning algorithm targeted towards minimization of motion sickness among passengers of automated vehicles, through targeted reduction of low-frequency accelerations. A Deep Reinforcement Learning (DRL) framework was utilised along with the design of a custom environment and a reward function which incorporates a measure of nauseogenicity of the planned trajectories. The frequency shaping effect of the reward function was evaluated by comparing against a DRL agent trained to optimize general motion comfort described by total acceleration energy. It was found that the nauseogenicity was reduced by 9.6% with the proposed DRL agent.

Further, on-road trials were performed with human drivers to establish a benchmark of driving comfort. The performance of the DRL agent was compared to human drivers as well as against an optimization-based motion planner that computationally maximizes the reward function. The DRL planner displayed comparable performance to the human drivers, and was within 10 to 15% of the discomfort levels of the optimization-based planner for a range of travel times. Meanwhile, the DRL planner offered notable improvements in computational efficiency, taking 1-2 ms to generate a sub-optimal trajectory, as opposed to approximately 5 s as taken by the optimization-based planner.